Preeclampsia: novel insights from global RNA profiling of trophoblast subpopulations
Notice bibliographique
Résumé
BACKGROUND: The maternal signs of preeclampsia, which include the new onset of high blood pressure, can occur because of faulty placentation. We theorized that transcriptomic analyses of trophoblast subpopulations in situ would lend new insights into the role of these cells in preeclampsia pathogenesis. OBJECTIVE: Our goal was to enrich syncytiotrophoblasts, invasive cytotrophoblasts, or endovascular cytotrophoblasts from the placentas of severe preeclampsia cases. Total RNA was subjected to global transcriptional profiling to identify RNAs that were misexpressed compared with controls. STUDY DESIGN: This was a cross-sectional analysis of placentas from women who had been diagnosed with severe preeclampsia. Gestational age-matched controls were placentas from women who had a preterm birth with no signs of infection. Laser microdissection enabled enrichment of syncytiotrophoblasts, invasive cytotrophoblasts, or endovascular cytotrophoblasts. After RNA isolation, a microarray approach was used for global transcriptional profiling. Immunolocalization identified changes in messenger RNA expression that carried over to the protein level. Differential expression of non-protein-coding RNAs was confirmed by in situ hybridization. A 2-way analysis of variance of non-coding RNA expression identified particular classes that distinguished trophoblasts in cases vs controls. Cajal body foci were visualized by coilin immunolocalization. RESULTS: Comparison of the trophoblast subtype data within each group (severe preeclampsia or noninfected preterm birth) identified many highly differentially expressed genes. They included molecules that are known to be expressed by each subpopulation, which is evidence that the method worked. Genes that were expressed differentially between the 2 groups, in a cell-type-specific manner, encoded a combination of molecules that previous studies associated with severe preeclampsia and those that were not known to be dysregulated in this pregnancy complication. Gene ontology analysis of the syncytiotrophoblast data highlighted the dysregulation of immune functions, morphogenesis, transport, and responses to vascular endothelial growth factor and progesterone. The invasive cytotrophoblast data provided evidence of alterations in cellular movement, which is consistent with the shallow invasion often associated with severe preeclampsia. Other dysregulated pathways included immune, lipid, oxygen, and transforming growth factor-beta responses. The data for endovascular cytotrophoblasts showed disordered metabolism, signaling, and vascular development. Additionally, the transcriptional data revealed the differential expression in severe preeclampsia of 2 classes of non-coding RNAs: long non-coding RNAs and small nucleolar RNAs. The long non-coding RNA, urothelial cancer associated 1, was the most highly up-regulated in this class. In situ hybridization confirmed severe preeclampsia-associated expression in syncytiotrophoblasts. The small nucleolar RNAs, which chemically modify RNA structure, also correlated with severe preeclampsia. Thus, we enumerated Cajal body foci, sites of small nucleolar RNA activity, in primary cytotrophoblasts that were isolated from control and severe preeclampsia placentas. In severe preeclampsia, cytotrophoblasts had approximately double the number of these foci as the control samples. CONCLUSION: A laser microdissection approach enabled the identification of novel messenger RNAs and non-coding RNAs that were misexpressed by various trophoblast subpopulations in severe preeclampsia. The results suggested new avenues of investigation, in particular, the roles of PRG2, Kell blood group determinants, and urothelial cancer associated 1 in syncytiotrophoblast diseases. Additionally, many of the newly identified dysregulated molecules might have clinical utility as biomarkers of severe preeclampsia.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».